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X-ORIGINAL-URL:https://www.eurandom.tue.nl
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TZOFFSETFROM:+0000
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DTSTART:20180101T000000
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BEGIN:VEVENT
DTSTART;TZID=UTC:20181005T150000
DTEND;TZID=UTC:20181005T154500
DTSTAMP:20201020T223618
CREATED:20180926T124708Z
LAST-MODIFIED:20190925T094612Z
UID:2279-1538751600-1538754300@www.eurandom.tue.nl
SUMMARY:Eindhoven Stochastics Seminar
DESCRIPTION:Joris Mulder (Tilburg University) \nThe Matrix-F Prior for Estimating and Testing Covariance Matrices \nThe matrix-F distribution is presented as prior for covariance matrices as an alternative to the conjugate inverted Wishart distribution. A special case of the univariate F distribution for a variance parameter is equivalent to a half-t distribution for a standard deviation\, which is becoming increasingly popular in the Bayesian literature. The matrix-F distribution can be conveniently modeled as a Wishart mixture of Wishart or inverse Wishart distributions\, which allows straightforward implementation in a Gibbs sampler. By mixing the covariance matrix of a multivariate normal distribution with a matrix-F distribution\, a multivariate horseshoe type prior is obtained which is useful for modeling sparse signals. Furthermore\, it is shown that the intrinsic prior for testing covariance matrices in non-hierarchical models has a matrix-F distribution. This intrinsic prior is also useful for testing inequality constrained hypotheses on variances. Finally through simulation it is shown that the matrix-variate F distribution has good frequentist properties as prior for the random effects covariance matrix in generalized linear mixed models. \n \n
URL:https://www.eurandom.tue.nl/event/eindhoven-stochastics-seminar-4/
LOCATION:MF 11-12 (4th floor MetaForum Building\, TU/e)
CATEGORIES:STO Seminar
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